Why now

Two 2026 agendas. One missing layer.

The two most-read startup agendas of this cycle — Y Combinator’s Request for Startups and a16z’s Big Ideas — describe the same gap from opposite ends. Companies now run many models and many agents, and nobody owns the layer that decides which one answers, what it costs, and whether the answer was any good. That layer is what Qua builds.

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The two agendas

What each one actually asks for.

Summarised in our own words, dated, and with no claim of endorsement — neither firm has reviewed, funded or commented on Qua.

Y Combinator

Software built for agents, not just for people

YC’s Requests for Startups keep circling one idea: the software stack assumes a human at the keyboard, and agents break that assumption. They ask for the infrastructure agents need to be trusted at work — permissions, auditability, verification of output, and a way to hand an agent a task without handing it the whole company.

  • Agents need scoped permissions, not shared logins.
  • Agent output needs verification before it becomes a business record.
  • Someone has to be accountable for what an agent spent and produced.

ycombinator.com/rfs

Andreessen Horowitz

Big Ideas 2026 — the enterprise orchestration layer

a16z’s enterprise theme for 2026 is orchestration: model choice has stopped being a single decision. Frontier, fast and open models each win different tasks, prices move monthly, and the winning enterprise software is the layer that routes work across them, keeps data inside policy, and proves the trade-off to finance.

  • One model per company is already an obsolete assumption.
  • Routing is an economic decision, made per task, thousands of times a day.
  • Without evidence, AI budgets get cut in the next planning cycle.

Read the enterprise orchestration discussion

Sources: Y Combinator’s Request for Startups pages, agents-at-work themes published through 2026; a16z’s Big Ideas 2026 collection, enterprise section, published December 2025. Both are third-party publications describing a market, not statements about Qua. Qua is not affiliated with, endorsed by or sponsored by either firm.
The three pillars

Simple on the surface. Governed underneath.

Multi-player

Your team and AI in the same room

One shared link. Colleagues see the same task, correct it mid-flight, approve a step, or hand it over. AI work stops being a private chat window nobody else can audit.

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Multi-Layer

The cheapest route that still answers well

Every question tries your own knowledge and approved company sources first, then a fast model, and only escalates to a premium model when it is genuinely worth it.

See the routing detail →
Multi-Model

Open, private, or frontier — under your rules

Commercial models, open-weight models, or your own fine-tune. Policy is checked before anything is sent, and the sensitive work can stay inside your walls.

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The map

Each ask, and the part of Qua that answers it.

What the agenda asks forWhat exists in Qua today
Scoped permissions for agentsThree independent authorization axes — organisation, platform, session — with per-provider blocks, per-task-class tier clamps and masking applied before a prompt is routed anywhere.
Verification before output becomes a recordA shared session with owner, reviewer and observer roles: an answer is only admitted to the company’s verified cache after a named human accepts it.
Accountability for spendA receipt on every answer — model, tokens, seconds, dollars, and what the same work would have cost on a premium baseline — rolled up per person, team and month.
Routing across many modelsA resolution waterfall: your own knowledge and verified answers first at zero cost, then fast models, and pro models only when the expected value of escalating clears a gate.
Proof for financeA value-proof scoreboard with weekly rollups, every figure labelled measured or estimated, exportable for a budget review.
Data staying inside policyDeployment in Qua Cloud, in your own cloud account, or fully inside your perimeter — with the same policy engine and the same audit trail in each.
Pilot evidence

76% less spent on AI tokens.

76%

Median reduction in AI token spend versus sending the same work straight to a frontier model. Repetitive, knowledge-heavy work saves more; exploratory research saves less. Every number is reproducible from the per-answer cost record in the product.

Price your own workload →Deployment options